Penguins

Welcome to SPICE 2023

This code chunk is where we load in all of the packages that we will use in this script using library(packagename)

• Packages allow us to interact with our data through the use of functions -> function(data)

• If you get the error: “package”packagename” does not exist”:

  • You need to install this package using install.packages(“packagename”) in the console below

Here is where we read in our data

• We use read_csv from the tidyverse because the file (dataset) we want to read in is a csv (comma separated values)

• We use here to cut out the long file paths and start within our R project

  • Within our project, the data is in the data folder so we direct our computer to the file with “data/dataset_name”

penguins_data <- read_csv(here("data/penguins_data/penguins_lter.csv"))

## Rows: 344 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): studyName, Species, Region, Island, Stage, Individual ID, Clutch C...
## dbl  (7): Sample Number, Culmen Length (mm), Culmen Depth (mm), Flipper Leng...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Clean column names of the dataframe using the clean_names function from the janitor package

• The %>% is called a “pipe”. This allows us to make a new dataframe from an existing dataframe and apply new analysis techniques
  • The keyboard shortcut for a pipe is “command + shift + i”

penguins_data <- penguins_data %>% 
  clean_names()

Exploratory Analysis

Let’s begin by exploring some of the columns of our dataset

There is an island column - Let’s see how many different islands there are in the dataset and how many penguins from the study are on each island - We can use the ‘table’ function to accomplish this

table(penguins_data$island)

## 
##    Biscoe     Dream Torgersen 
##       168       124        52

Now, let’s look at the body mass column by creating a histogram

ggplot(data = penguins_data, aes(x = body_mass_g)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 2 rows containing non-finite values (`stat_bin()`).

This looks good, now let’s clean this up and look at body mass by species of penguin

ggplot(data = penguins_data, aes(x = body_mass_g,
                                 fill = species)) +
  geom_histogram(color = "black") +
  theme_minimal() +
  labs(title = "Penguins, Palmer Station LTER",
       subtitle = "Body Mass Distribution for Adelie, Chinstrap and Gentoo Penguins",
       x = "Body mass (g)",
       y = "Number of Penguins",
       color = "Penguin species")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 2 rows containing non-finite values (`stat_bin()`).

Now let’s look at the relationship between flipper length and body mass with a scatter plot

ggplot(data = penguins_data, aes(x = flipper_length_mm, 
                                 y = body_mass_g)) +
         geom_point()

## Warning: Removed 2 rows containing missing values (`geom_point()`).

It looks like there is a strong positive correlation between body mass and flipper length - Let’s add some variables from the dataset - Island - Species - Sex

ggplot(data = penguins_data, aes(x = flipper_length_mm, 
                                 y = body_mass_g,
                                 color = species,
                                 shape = sex)) +
         geom_point() +
  theme_minimal() +
  labs(title = "Penguin size, Palmer Station LTER",
       subtitle = "Flipper length and body mass for Adelie, Chinstrap and Gentoo Penguins",
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin species",
       shape = "Penguin sex") +
  theme(axis.text.x = element_text(angle = 45)) +
  facet_grid(~island)

## Warning: Removed 10 rows containing missing values (`geom_point()`).

Now let’s explore the relationships between our numeric columns in the dataset with a correlation matrix

Select numeric columns for correlations

penguins_data_numeric <- penguins_data %>% 
  select(culmen_length_mm, culmen_depth_mm, flipper_length_mm, body_mass_g)

Create a correlation matrix

cor_matrix <- cor(penguins_data_numeric[complete.cases(penguins_data_numeric), ], use = "pairwise.complete.obs")

Plot the correlation matrix

corrplot <- ggcorrplot(cor_matrix, type = "lower", outline.color = "white") +
  theme(axis.text.x = element_text(size = 3),
        axis.text.y = element_text(size = 3))
corrplot

Make the correlation plot interactive

ggplotly(corrplot)